Detection of living <i>Bursaphelenchus xylophilus</i> in wood, using reverse transcriptase loop‐mediated isothermal amplification (RT‐LAMP)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Pinewood nematode (PWN), Bursaphelenchus xylophilus , the causal agent of pine wilt disease, is an inhabitant of native pine species of North America, where its presence has minor impact. In contrast, the introduction of this nematode to forests in Asia and Europe has devastated some pine stands and is recognized as a pest of significant phytosanitary concern by the National Plant Protection Organizations of several countries. The ability to detect PWN in internationally traded wood products is crucial to reduce the spread of this organism. Currently, the majority of molecular techniques for the detection of PWN rely on the presence of genomic DNA and thus fail to differentiate between living and dead PWN. The detection of dead nematodes could lead to unnecessary trade disruption. Therefore, accurate techniques for the detection of and differentiation between living and dead PWN are critical. We have developed a reverse transcription loop‐mediated isothermal amplification (RT‐LAMP) assay, which specifically identifies living PWN in wood by detecting the presence of mRNA encoding an expansin gene as a viability marker. This diagnostic method was found to be more sensitive, faster and less dependent on expensive laboratory equipment than PCR. In addition, unlike PCR, it allows for simple colour detection of amplification products. This method will help resolve disputes over the detection of PWN by clarifying whether it originates from live or dead organisms. Where approved treatments are implemented, unnecessary trade disruption will be avoided, thus protecting market access of wood products from PWN‐infested areas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it